1. Field of the Invention:
The invention relates generally to a method and system for the computerized analysis of bone mass and structure. Specific applications are given for the analysis of the trabecular mass and bone pattern for the assessment of bone strength and/or osteoporosis and as a predictor of risk of fracture. Novel techniques involve the merging of various features including those related to bone mass, bone geometry, bone structural information, and subject""s age. Additional techniques include the application of Minkowski Dimension and an artificial neural network to aid in the computerized fractal analysis of the bone structure. In addition, an estimate of the volumetric BMD is presented incorporating bone mass and bone geometry.
The present invention generally relates to computerized techniques for automated analysis of digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; and 5,740,268; as well as U.S. patent application Ser. Nos. 08/158,388; 08/173,935; 08/220,917; 08/398,307; 08/428,867; 08/523,210; 08/536,149; 08/536,450; 08/515,798; 08/562,087; 08/757,611; 08/758,438; 08/900,191; 08/900,361; 08/900,362; 08/900,188; and 08/900,189, 08/900,192; 08/979,623; 08/979,639; 08/982,282; 09/027,468; 09/027,685; 09/028,518; 09/053,798; 09/092,004; 09/098,504; 09/121,719; and 09/131,162 all of which are incorporated herein by reference.
The present invention includes use of various technologies referenced and described in the above-noted U.S. Patents and Applications, as well as described in the references identified in the appended APPENDIX and cross-referenced throughout the specification by reference to the number, in brackets and bold print, of the respective reference listed in the APPENDIX, the entire contents of which, including the related patents and applications listed above and references listed in the APPENDIX, are incorporated herein by reference.
2. Discussion of the Background:
Although there are many factors that affect bone quality, two primary determinants of bone mechanical properties are bone mineral density (BMD) and bone structure. Among the density and structural features extracted from bone using various techniques, researchers agree that BMD is the single most important predictor of bone strength as well as disease-conditions such as osteoporosis. Studies have shown correlation between BMD and bone strength (Carter and Haye, 1977 [4]; Beck et al., 1989 [2]; Keaveny and Hayes, 1993 [9]). To this purpose, a range of techniques have been developed to measure BMD to evaluate fracture risk, diagnose osteoporosis, monitor therapy of osteoporosis, and predict bone strength (Beck et al., 1989 [2]; Ross et al., 1990 [14]; Adams, 1997 [1]; Grampp et al., 1997 [7]).
The standard technique for noninvasive evaluation of bone mineral status is bone densitometry. Among various techniques for bone densitometric measurement, dual energy X-ray absorptiometry (DXA) is relatively inexpensive, low in radiation dose ( less than 5 xcexcSv effective dose equivalent), and of high accuracy (≈1%) and precision (≈1%) (Sartoris and Resnick, 1990 [15]; Adams, 1997 [1]; Lang, 1998 [10]). DXA has gained widespread clinical acceptance for the routine diagnosis and monitoring of osteoporosis (Adams, 1997 [1]). In addition, DXA can be directly used to measure whole bone geometric features (Faulkner et al., 1994 [6]; Sieranen et al., 1994 [17]; Karlsson et al., 1996 [8]; Lang, 1998 [10]). The BMD measurement from DXA, however, is only moderately correlated to bone mechanical properties and has limited power in separating the patients with and without osteoporosis-associated fractures (Cann et al., 1985 [3]). DXA provides an integral measure of cortical and trabecular bone mineral content along the X-ray path for a given projected area, but DXA only measures bone mass, not bone structure. As a consequence, DXA measurements are bone-size dependent and yield only bone mineral density per unit area (g/cm2) instead of true density, i.e., volumetric bone mineral density (g/cm3). Therefore, if the BMD measurements of patients with different bone sizes are compared. the results can be misleading.
Although the effect of bone size on area BMD using DXA is apparent (Carter et al., 1992 [5]; Seeman, 1998 [16]), only a few studies (Nielesn et al., 1980 [13]; Martin and Buff, 1984 [11]; Carter et al., 1992 [5]) have been performed to account for such a bias. To compensate for the effect of bone size for vertebral bodies, Carter et al. (1992) [5] developed an analysis method and suggested a new parameter, bone mineral apparent density (BMAD), as a measure of volumetric bone mineral density.
Also, one of the functions of bone is to resist mechanical failure such as fracture and permanent deformation. Therefore, biomechanical properties are fundamental measures of bone quality. The biomechanical properties of trabecular bone are primarily determined by its intrinsic material properties and the macroscopic structural properties (Cowin et al., 1987 [24]; Chakkalakl et al., 1990 [23]; Brandenburger, 1990 [21]; Keaveny and Hayes, 1993 [9]). Extensive efforts have been made toward the evaluation of bone mechanical properties by studying bone mineral density (BMD) and mineral distribution.
Since bone structural rigidity is derived primarily from its mineral content (Elliott et al., 1989 [27]), most evaluation methods have been developed to measure bone mass (mineral content or density) and to relate these measures to bone mechanical properties (Carter and Haye, 1977 [4]; Bentzen et al., 1987 [20]; Hvid et al., 1989 [32]; Keaveny and Hayes, 1993 [9]; Keaveny et al., 1994 [36]). Results from in vivo and in vitro studies suggest that BMD measurements are only moderately correlated to bone strength (Carter et al., 1992 [5]). However, studies have shown changes in bone mechanical properties and structure independent of BMD (Goldstein, 1987 [30]; Faulkner et al., 1991 [28]). Moreover, because density is an average measurement of bone mineral content within bone specimens, density does not include information about bone architecture or structure.
Various methods have been developed for in vitro study of two- or three-dimensional architecture of trabecular bones using histological and stereological analyses (Whitehouse, 1974 [31]; Feldkamp et al., 1989 [29]; Goulet et al., 1994 [31]; Croucher et al., 1996 [25]). These studies have shown that, by combining structural features with bone density, about 72 to 94 percent of the variability in mechanically measured Young""s moduli could be explained. However, these measurements are. invasive.
For the noninvasive examination of trabecular bone structure, investigators have developed high-resolution computed tomography (CT) and magnetic resonance imaging (MRI) (Feldkamp et al., 1989 [29]; Durand and Ruegsegger, 1992 [26]; Majumder et al., 1998 [38]). However, due to cost and/or other technical difficulties, these techniques are currently not in routine clinical use. The potential of using X-ray radiographs to characterize trabecular bone structure has also been studied. Although the appearance of trabecular structure on a radiograph is very complex, studies have suggested that fractal analysis may yield a sensitive descriptor to characterize trabecular structure from x-ray radiographs both in in vitro studies (Majumdar et al, 1993 [37]; Benhamou et al., 1994 [19]; Acharya et al., 1995 [18]; Jiang et al., 1998a [33]) and in an in vivo study (Caligiuri et al., 1993 [22]).
Different methods, however, exist with which to compute fractal dimension. Minkowski dimension, a class of fractal dimension that is identical to Hausdroff dimension (Mandelbrot, 1982 [39]), is particularly suitable for analyzing the complex texture of digital images because it can be formally defined through mathematical morphology and easily computed using morphological operations (Serra, 1982 [42]; Maragos, 1994 [40]). The Minkowski dimension computed from an image, regardless of texture orientation, gives a global dimension that characterizes the overall roughness of image texture. Similarly, the Minkowski dimensions computed from different orientations yield directional dimensions that can be used to characterize the textural anisotropy of an image (Jiang et al., 1998a [33]).
Accordingly, an object of this invention is to provide a method and system for the computerized analysis of bone mass and/or structure.
Another object of this invention is to provide a method and system for estimating bone strength.
Another object of this invention is to provide a method and system for estimating a volumetric bone mass measure using bone geometry.
Another object of this invention is to provide a method and system for incorporating Minkowski Dimension into the analysis of the bone structure pattern.
Another object of this invention is to provide a method and system for extracting information from fractal-based texture analyses.
Another object of this invention is to provide a method and system for merging information on bone mass, bone geometry, bone structure and/or subject age in order to obtain measures of bone strength.
These and other objects are achieved according to the invention by providing a novel automated method, storage medium storing a program for performing the steps of the method, and system in which digital image data corresponding to an image of the bone are obtained. Next there is determined, based on the digital images, a measure of bone mineral density (BMD) and at least one of a measure of bone geometry, a Minkowski dimension, a trabecular orientation, and subject data. The strength of the bone is estimated based upon the measure of BMD and at least one of the measure of bone geometery, the Minkowski dimension, the trabecular orientation, and the subject data. Preferably, a normalized BMD corresponding to a volumetric bone mineral density of the bone as the measure of BMD is determined, and the strength of the bone is estimated based at least in part on the normalized BMD.
To improve bone texture analysis, the present invention also provides a novel automated method, storage medium storing a program for performing the steps of the method, and system in which digital image data corresponding to an image of the bone is obtained, and a region of interest (ROI) is selected within the bone. A fractal characteristic of the image data within the ROI using an artificial neural network is extracted. The strength of the bone is estimated based at least in part on the extracted fractal characteristic.
To perform bone analysis with an improved measure of bone mineral density, the present invention also provides a novel automated method, storage medium storing a program for performing the steps of the method, and system in which digital image data corresponding to an image of the bone is obtained. A measure of normalized bone mineral density (BMD) corresponding to a volumetric bone mineral density of the bone is determined, and the strength of the bone based is estimated based at least in part on the normalized BMD.